Managing Both Organized and Disorganized Data for Healthcare Institutions: A Step-by-Step Guide
In the rapidly evolving world of healthcare, managing vast amounts of data has become a critical challenge. With unstructured data making up 80% of all clinical content, organizations face the daunting task of storing and normalizing this data to ensure its accuracy, consistency, and compliance with regulations.
The sheer volume of data generated daily is staggering. According to estimates, healthcare organizations generate approximately 50 petabytes of data per year, more than twice the amount of data in the Library of Congress. A single chest X-ray is around 15 megabytes, a 3D mammogram can be 300 megabytes, and a digital pathology file can be 3 gigabytes. This data amounts to 137 terabytes per day.
Artificial intelligence (AI) and natural language processing can help normalize unstructured data, but coding it to applicable industry standards like ICD-10 or SNOMED is equally important. This process brings order to unstructured data, making it look more like structured data.
Effective data governance is essential for maintaining the quality and security of patient data, which is crucial for informed decision-making and compliance with industry regulations like HIPAA. By establishing clear policies and procedures, data governance helps streamline operations, reduce costs, and prevent data breaches or errors that could harm patients or damage an organization's reputation.
Proper data management ensures adherence to evolving legal standards, minimizing the risk of legal and financial repercussions. The American Hospital Association suggests that hospitals should transform into data-driven organizations to improve decision-making and better serve patients.
Data governance supports data-driven decision making (DDDM) by providing reliable data. This approach enhances patient care, nursing, and broader healthcare management by enabling predictive analytics and evidence-based policy development. Accurate and consistent data ensure that clinical decisions, such as diagnoses and treatment plans, are based on reliable information, directly impacting patient outcomes.
Implementing frameworks like the Data Governance Institute’s (DGI) Framework or the Health Information Trust Alliance (HITRUST) Common Security Framework (CSF) helps define governance roles and policies. Roles-based access and data stewardship are key components, ensuring that data is managed and accessed appropriately. Regular audits and continuous monitoring tools help identify compliance gaps and data issues, allowing for timely corrections.
The use of technologies like AI can automate data governance tasks, improve data quality, and enhance compliance. Optimizing storage by migrating data to the cloud can free up space onsite for the most recent and relevant data. Classifying data based on how it will be used, who needs to access it, what level of confidentiality it needs, and what security policies apply to it is important.
However, the proliferation of unstructured data in healthcare can pose challenges related to data retention, purging, and destruction. Organizations often purge medical records that are inactive or delete research data sets once a study has been completed. Becoming a data-driven organization depends on the ability to derive meaning from unstructured data.
Unstructured data in healthcare includes medical images and written narratives like clinical notes, problem lists, discharge summaries, and radiology reports. This data often captures the severity of a patient's health condition or nuanced nonclinical social needs better than structured data. Clinical teams should provide data science teams with appropriate guidance before they dive into a data set.
In summary, data governance is fundamental to the successful management and decision-making processes in healthcare, ensuring that data is well-managed, secure, and effectively utilized for improved patient care and operational efficiency. Brian Laberge, a Solution Engineer at Wolters Kluwer, emphasizes the importance of focusing on a key business metric or other quantifiable area of improvement when dealing with unstructured data in healthcare. By doing so, healthcare organizations can unlock the immense value hidden within their unstructured data, ultimately improving patient outcomes and operational efficiency.
[1] HIPAA: Health Insurance Portability and Accountability Act [2] DDDM: Data-Driven Decision Making [3] HITRUST: Health Information Trust Alliance [4] EHRs: Electronic Health Records [5] AI: Artificial Intelligence
- In the realm of science and technology, the integration of artificial intelligence (AI) and natural language processing can significantly aid in normalizing unstructured medical-conditions data within health-and-wellness organizations.
- To ensure the efficient storage and utilization of home-and-garden-sized data within the healthcare sector, effective data-governance policies and frameworks like the Data Governance Institute’s (DGI) Framework or the Health Information Trust Alliance (HITRUST) Common Security Framework (CSF) should be implemented.
- The use of data-driven decision making (DDDM) in the lifestyle context of healthcare can positively impact patient care, nursing, and broader healthcare management by enabling accurate diagnoses, predictive analytics, and evidence-based policy development through the effective use of data and technology.
